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arXiv · 2510.16031

A Storm-Centric 250 m NEXRAD Level-II Dataset for High-Resolution ML Nowcasting

Andy Shi
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Abstract

Machine learning-based precipitation nowcasting relies on high-fidelity radar reflectivity sequences to model the short-term evolution of convective storms. However, the development of models capable of predicting extreme weather has been constrained by the coarse resolution (1-2 km) of existing public radar datasets, such as SEVIR, HKO-7, and GridRad-Severe, which smooth the fine-scale structures essential for accurate forecasting. To address this gap, we introduce Storm250-L2, a storm-centric radar dataset derived from NEXRAD Level-II and GridRad-Severe data. We algorithmically crop a fixed, high-resolution (250 m) window around GridRad-Severe storm tracks, preserve the native polar geometry, and provide temporally consistent sequences of both per-tilt sweeps and a pseudo-composite reflectivity product. The dataset comprises thousands of storm events across the continental United States, packaged in HDF5 tensors with rich context metadata and reproducible manifests.

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Andy Shi. 2025-10-15. A Storm-Centric 250 m NEXRAD Level-II Dataset for High-Resolution ML Nowcasting. https://arxiv.org/abs/2510.16031

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